Individual tree crowns delineation using local maxima approach and seeded region growing technique

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67179843%3A_____%2F11%3A00359864" target="_blank" >RIV/67179843:_____/11:00359864 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Individual tree crowns delineation using local maxima approach and seeded region growing technique

Popis výsledku v původním jazyce

Remote sensing applications in forestry can profit from a rapid development of optical sensors. Applied algorithms should be suited to the new properties of the data (very high resolution and wide spectral range). Segmentation of the image into objects is an important task in forestry applications of optical remote sensing. Segmentation of individual tree crowns involves two parts ? detection and delineation phase. Local maxima approach and seeded region growing technique are presented as the key concepts. Furthermore improvements, namely histogram equalization and Voronoi diagram, are incorporated. Two datasets were processed and results of the segmentation are presented. Hyperspectral data with spatial resolution of 0.8m were found as a suitable forsegmentation process with 84% and 78% accuracy of detection phase and 64% and 52% accuracy in delineation phase respectively. Finally discussion of recommended settings in the algorithm is presented.

Název v anglickém jazyce

Individual tree crowns delineation using local maxima approach and seeded region growing technique

Popis výsledku anglicky

Remote sensing applications in forestry can profit from a rapid development of optical sensors. Applied algorithms should be suited to the new properties of the data (very high resolution and wide spectral range). Segmentation of the image into objects is an important task in forestry applications of optical remote sensing. Segmentation of individual tree crowns involves two parts ? detection and delineation phase. Local maxima approach and seeded region growing technique are presented as the key concepts. Furthermore improvements, namely histogram equalization and Voronoi diagram, are incorporated. Two datasets were processed and results of the segmentation are presented. Hyperspectral data with spatial resolution of 0.8m were found as a suitable forsegmentation process with 84% and 78% accuracy of detection phase and 64% and 52% accuracy in delineation phase respectively. Finally discussion of recommended settings in the algorithm is presented.

Druh

D - Stať ve sborníku

CEP obor

EH - Ekologie – společenstva

OECD FORD obor

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Projekt

<a href="/cs/project/SP%2F2D1%2F70%2F08" target="_blank" >SP/2D1/70/08: ForChange - indikace stavu a změn lesních ekosystémů v kontextu globální změny aplikací analýzy ekofyziologických procesů a sledování s využitím metod DPZ</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

GIS Ostrava 2011. Eight international symposium. Ostrava, Czech Rrepbublic , January 2011

ISBN

978-80-248-2406-2

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

27-39

Název nakladatele

Technical university

Místo vydání

Ostrava

Místo konání akce

Ostrava

Datum konání akce

24. 1. 2011

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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